I gave a seminar this week at ICRIER (Indian Council for Research
on International Economic Relations) where I argued that it is today
computationally feasible to implement a risk management system for
derivative exchanges that is (a) based on Expected Shortfall, (b)
incorporates fat tailed distributions and (c) computes portfolio risk
across multiple underlyings (securities or commodities) using non
linear dependence models (copulas).

Risk measures like Value at Risk, SPAN and Risk Metrics have their
intellectual roots in the early 1990s or earlier when the notion of
coherent risk measures had not been developed and risk modelling had
not yet embraced fat tailed distributions with non linear dependence
structures. For example, current global best practice in handling
exposure to multiple underlyings (“inter commodity
spreads”) in exchange risk management can only be characterized
as crude and ad hoc. Their continued popularity owes much to the
inadequacies of correlation based dependency modelling. Similarly, the
SPAN framework uses too few scenarios to meet the highly desirable
“relevance axiom” for risk measures
though computational advances allow us to come very close to
fulfilling this axiom.

In India, the regulatory framework for risk management at
Indian exchanges is still supposed to be based on the 99% value at
risk mandated by the L C Gupta Committee a decade ago. In practice,
however, Indian exchanges and their regulators have adopted several
features of a fat tailed expected shortfall approach. Risk management
practice has thus outgrown the regulatorily mandated value at risk to
which it still pays lip service. The time has come to formally discard
value at risk from the regulatory lexicon and adopt a more modern
vocabulary. This would provide an opportunity to spur new research on
improving exchange risk management systems.

My presentation made specific proposals for a modern risk
management system and indicated directions for further research. The
slides of this presentation are available here.